Introduction
Cyberattacks are getting faster, putting security teams under immense pressure to respond before damage is done. Traditional response models, like CrowdStrike’s 1/10/60 framework, were once a strong benchmark but now struggle to keep up with attackers who can move laterally in under 30 minutes. AI-driven automation changes the equation by cutting investigation and response times to minutes instead of hours. AI SOC analysts help organizations avoid evolving threats by reducing manual workloads and enabling immediate action.
The Speed of Modern Cyberattacks
Scattered Spider Attacks
Scattered Spider, a financially motivated threat group, has refined its use of social engineering to infiltrate major organizations in record time. Their techniques often include phishing campaigns, SIM swapping, and MFA fatigue attacks, allowing them to compromise accounts and gain administrative access in hours.
A high-profile example was the MGM Resorts breach, where attackers gained access through a phone-based social engineering attack and rapidly escalated privileges within a single day, leading to significant business disruption.
Merck’s NotPetya Attack
The NotPetya malware attack was one of the fastest-spreading cyber incidents in history. Within 90 seconds of initial execution, over 10,000 Merck devices were compromised, crippling the company’s global operations.
The malware, designed to look like ransomware, exploited Windows vulnerabilities to spread laterally without user interaction, encrypting entire systems and causing an estimated $1.3 billion in damages. The speed and scale of this attack demonstrated how traditional security models struggle to contain modern threats before widespread impact.
Coinbase Social Engineering Case Study
Coinbase, a company with strong security controls, experienced an advanced social engineering attack that showed how even well-trained employees can be manipulated. Attackers used a SMS phishing campaign and social engineering techniques to attempt to bypass authentication barriers.
The attackers got credentials through phishing but couldn’t get past MFA. They called the target and pretended to be with the IT team when the actual Coinbase CSIRT called the employee. Because of Coinbase’s fast response, a breach was avoided. The incident highlights how quickly IT security teams need to respond to avoid incidents.
Why Traditional Response Models Fall Short
CrowdStrike’s 1/10/60 Framework (2021)
The 1/10/60 framework set a standard for cyber defense, recommending that organizations aim to detect threats within one minute, investigate within ten minutes, and contain them within sixty minutes.
While this was a good benchmark in 2021, today’s threats move even faster. Adversaries now achieve breakout times of 20 minutes or less, meaning attackers can escalate privileges, move laterally, and deploy payloads before defenders can respond.
This acceleration forces security teams to rethink their response strategies. The window for stopping an attacker before significant damage occurs has shrunk, and traditional response models relying on human analysts for initial triage and containment decisions are struggling to keep up. Without faster automated investigation and response, organizations that meet the 1/10/60 standard may still experience data breaches, service disruptions, and operational downtime before a full containment effort is completed.
Limitations of Manual Investigations
SOC analysts deal with a constant flood of alerts, many requiring deep investigation before they can be dismissed or escalated. Even well-staffed security teams often find it difficult to keep up, and alert fatigue leads to slower response times and missed threats.
Manual triage, log correlation, and verification processes introduce delays, giving attackers more time to establish persistence and expand their foothold. With modern attacks evolving rapidly, relying solely on human-driven investigations means defenders are always one step behind.
SOAR Automation Helps, But Has Limits
SOAR platforms automate repetitive security tasks, such as collecting contextual data, enriching alerts, and executing predefined playbooks. While this reduces the burden on analysts, SOAR struggles with complex investigations that require reasoning beyond simple if-then logic. Playbooks rely on predefined decision trees, meaning they cannot adapt dynamically to unexpected attack patterns. When an alert does not fit an existing automation rule, it still requires human intervention, delaying response efforts. The Coinbase example above shows the limits of SOAR, which cannot automate user interviews.
SOAR remains valuable for structured response workflows, but it alone is not enough to handle the speed and complexity of modern attacks. Security teams need real-time automation that can adapt to the multitude of permutations that real-world alert investigations take, rather than relying only on static workflows.
How AI Outperforms the 1/10/60 Response Framework
Alert Investigations That Start Immediately
AI SOC analysts begin investigations as soon as an alert enters the queue, significantly reducing mean time to acknowledge (MTTA). Instead of waiting for an analyst to pick up an alert, AI automatically retrieves relevant logs, correlates events, and analyzes potential threats.
Within minutes, AI can determine if an alert is a false positive or a legitimate security risk, allowing security teams to focus only on incidents that require human expertise. In many cases, AI SOC analysts complete full investigations in under 10 minutes, dramatically reducing response delays.
Accelerating Response to Social Engineering Attacks
For attacks that involve phishing or otherwise require user interviews, AI SOC analysts can even conduct those interviews, significantly cutting down the latency that’s inherently part of those back-and-forth conversations.
Read this blog to learn more about Dropzone AI’s AI interviewer feature. Or, watch the product tour below to see how it works.
Automated Response Actions
Once an investigation is complete, AI SOC analysts integrate with existing security tools to take action immediately. AI can block malicious IPs, revoke compromised credentials, and quarantine infected endpoints without waiting for human approval.
When an investigation leads to an escalation, AI can automatically create cases in ServiceNow, send alerts to Slack, and generate detailed reports for review. These automated workflows help security teams move faster while allowing them to intervene when necessary.
Achieving Detection to Remediation in <20 Minutes
Organizations can detect, investigate, and contain threats in under 20 minutes by combining AI-driven investigations with automated response actions. AI eliminates delays in triage and investigation, while automated workflows allow immediate remediation.
AI can operate in a hybrid mode for teams that prefer human oversight. In this mode, recommended actions are presented for analyst approval before execution. This flexible approach helps organizations reduce risk while maintaining full control over security operations.
Conclusion
Modern cyber threats don’t wait, and neither should security teams. The old detection and response models are too slow for attackers who can breach a system, escalate privileges, and exfiltrate data within minutes. AI SOC analysts shrink detection-to-remediation time to 20 minutes or less, allowing security teams to contain threats before they escalate. The best way to stay ahead is to adopt AI-driven security automation before attackers make the next move. Book a demo with Dropzone AI to learn more.
If you’re interested in learning how Dropzone AI can speed up Mean Time to Conclusion (MTTC), download this ebook: MTTC, the KPI for SOC Effectiveness.
